{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:36.212001Z",
     "start_time": "2017-12-21T12:06:36.201759Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 练习1：航班乘客变化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:36.624761Z",
     "start_time": "2017-12-21T12:06:36.605962Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949</td>\n",
       "      <td>January</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949</td>\n",
       "      <td>February</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949</td>\n",
       "      <td>March</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949</td>\n",
       "      <td>April</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949</td>\n",
       "      <td>May</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     month  passengers\n",
       "0  1949   January         112\n",
       "1  1949  February         118\n",
       "2  1949     March         132\n",
       "3  1949     April         129\n",
       "4  1949       May         121"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sns.load_dataset(\"flights\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 年度乘客总量变化情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:37.270115Z",
     "start_time": "2017-12-21T12:06:37.037164Z"
    },
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+ma0wsxQzW2JmKcfayMwqmtkiYBvwJbAG2O2cO3RrXTqBZ8vj/dwE4C3fA9QP\n/q2ISDR4ZdYa/t/0FVzavQlPXdFV4S5yBME20V9wIi/unCsEuptZHWAa0LGk1byfJf0v/dUjac1s\nFDAKoEULPfJRJJq89t1anvhsORd3a8LTV3ajosJd5IiCOoJ3zm0AmhM4p74ByA52W2/73cA3QF+g\njvc8eYBmwBZvPN37HYeeN18b2FnCa73qnEt0ziXGxcUFW4KIlHMTflzPY5+kceGpjXj2qm5Uqhj0\nR5BIVArqf4iZPQjcBdzjzYoBJh1jmzjvyB0zqwqcA6QBM4ErvNWGErhCH+BDbxpv+dfOuV8dwYtI\n9Hl99gYe/DCV8zo15PmreyjcRYIQbBP9ZUAPYAGAc26LmdU8xjaNgQlmVpHAF4mpzrmPzWwZMMXM\nHgMWAmO89ccAr5vZagJH7leX9KIiEl3eTN7IP95fyjkd43nx2p7EKNxFghJswOc555yZOQCvi9qj\ncs6lEPhScPj8tUCfEubnAFcGWY+IRIGp8zZx77QlDGgfx7+v60nlSgp3kWAF+79lqpm9QuD8+U3A\nDGB06MoSkWj33oJ07no3hdPaNODl63tRpVJFv0sSKVeC7Yv+aTM7l8BDZtoDDzjnvgxpZSIStT5Y\ntJn/eXsx/VrXZ/QNicTGKNxFjlewfdFXJ3DR25dm1h5ob2Yxzrn80JYnItHmk5QM/vbWInon1GPM\n0N4Kd5ETFGwT/bdAFTNrSqB5/kZgfKiKEpHo9PnSDP4yZSG9WtZl7LDeVK2scBc5UcEGvDnnsoHf\nAf/nnLsM6BS6skQk2nyRupU/vbGQbs1qM+7GPlSvEuw1wCJSkqAD3sz6AdcBn3jz9L9PRErF18sz\nufWNBXRuWpvxw/tQQ+EuctKCDfjbCHRyM805l2pmrQl0WCMiclJmrcziltcX0KFRLSYO70Ot2Bi/\nSxKJCMFeRT8LmAVgZhWA7c65v4SyMBGJfN+v2s5NE+fRJr4Gr4/oQ+2qCneR0hJsV7VvmFkt72r6\nZcAKM7sztKWJSCSbvXYHIyfOpXWD6kwemUSdapX9LkkkogTbRN/JObeXwLPbPwVaAENCVpWIRLT5\nG3YxYvxcmtWtxqSRSdStrnAXKW3BBnyMmcUQCPgPvPvf9SAYETluSzfvYdi4ZOJqVuGNkUk0qFHF\n75JEIlKwAf8KsB6oDnxrZi0J9GonIhK05Vv3cv2YOdSKjWHyTX2JrxXrd0kiESvYi+xeAF4oNmuD\nmQ0MTUkiEolWb9vP9a/NoUqlCrxxUxJN61T1uySRiBb0zaZmdhHQGSj+lfuRUq9IRCLOhh0HuO61\n2QBMHtmXlvWP+UBKETlJwfaPp53CAAAa0klEQVRF/zJQDRgIvAZcASSHsC4RiRCbdx/k2tFzyC0o\nYsqovrSJr+F3SSJRIdhz8L9xzt0A7HLOPQz0A5qHriwRiQSZe3O4dvRs9ubkM2lEEh0a1fK7JJGo\nEWzAH/R+ZptZEyAfaBWakkQkEmzfn8u1o2ezfV8uE4b3oUvT2n6XJBJVgj0H/7GZ1QGeAuZ7814L\nTUkiUt7tzs7j+tfmsHn3QSbc2IeeLer6XZJI1Ak24J8G/gCcDvwEfAe8FKqiRKT82puTzw1jk1m7\n/QBjhiaS1Lq+3yWJRKVgA34CsI//3ip3DTARuCoURYlI+XQgt4Abx81l2Za9vDKkF6e3jfO7JJGo\nFWzAt3fOdSs2PdPMFoeiIBEpn3LyCxk5YR6LNu3mxWt6cHbHhn6XJBLVgr3IbqGZ9T00YWZJwA+h\nKUlEypvcgkJGvT6f2et28MxV3bjg1MZ+lyQS9YI9gk8CbjCzjd50CyDNzJYAzjnXNSTViUjYyy8s\n4k9vLOTblVk8dXlXBndv6ndJIkLwAX9+SKsQkXKpoLCI295axJfLMnlkcGeu6q3uMUTCRbB90W8I\ndSEiUr4UFTn+/k4Kn6RkcN+FHbmhX4LfJYlIMcGegxcR+ZlzjvveX8p7Czdzx7ntuOmM1n6XJCKH\nUcCLyHFxzvHwR8t4M3kjfxxwCn86q43fJYlICRTwIhI05xz//HwF439cz/D+rbhzUHvMzO+yRKQE\nCngRCdrzX63i5VlruC6pBf/4bUeFu0gYU8CLSFBenrWG52as4opezXh0cBeFu0iYU8CLyDGN/2Ed\nT362nIu7NeGfl3elQgWFu0i4U8CLyFG9mbyRhz5axqDODXnmqm5UVLiLlAsKeBE5omkL07l32hIG\ntI/jhWt6EFNRHxki5YX+t4pIiT5JyeCOqYvp17o+L1/fiyqVKvpdkogch5AFvJk1N7OZZpZmZqlm\n9ldv/kNmttnMFnnDhcW2ucfMVpvZCjMbFKraROTovlyWyV+nLKRXy7q8NjSR2BiFu0h5E2xf9Cei\nALjDObfAzGoC883sS2/Zs865p4uvbGadgKuBzkATYIaZtXPOFYawRhE5zKyVWdw6eQGdm9Zm7LDe\nVKscyo8JEQmVkB3BO+cynHMLvPF9QBpwtMdMDQamOOdynXPrgNVAn1DVJyK/9tOaHYyaOI828TWY\neGMfasbG+F2SiJygMjkHb2YJQA9gjjfrT2aWYmZjzayuN68psKnYZumU8IXAzEaZ2Twzm5eVlRXC\nqkWiy/wNOxkxYS4t6lXj9RF9qF1N4S5SnoU84M2sBvAucJtzbi/wEnAK0B3IAP51aNUSNne/muHc\nq865ROdcYlxcXIiqFokuKem7GTZ2Lg1rxTJ5ZBL1a1TxuyQROUkhDXgziyEQ7pOdc+8BOOcynXOF\nzrkiYDT/bYZPB4o/TLoZsCWU9YkIpGXsZciYZGpXi2HyyCTia8X6XZKIlIJQXkVvwBggzTn3TLH5\njYutdhmw1Bv/ELjazKqYWSugLZAcqvpEBFZv28f1r82hakxF3rypL03qVPW7JBEpJaG8PLY/MARY\nYmaLvHn3AteYWXcCze/rgZsBnHOpZjYVWEbgCvxbdQW9SOis336Aa0fPwcx446Ykmter5ndJIlKK\nQhbwzrnvKfm8+qdH2eZx4PFQ1SQiAem7srnutTnkFxYxZVQ/WsfV8LskESll6slOJMps3ZPDtaPn\nsC8nn9dHJNG+UU2/SxKREFAPFiJRZPv+XK57bTY7D+Tx+og+dGla2++SRCREdAQvEiV2Hcjj+tfm\nsGV3DmOH9aZHi7rH3khEyi0dwYtEsPzCIpLX7eTzpVv5bOlW9ubkM25Yb/q0qud3aSISYgp4kQiT\nk1/I96u283nqVmakZbI7O5+qMRU5s10cw09rpXAXiRIKeJEIsC8nn5krspieupWZy7eRnVdIzdhK\nnNuxIYO6NOKMtnFUrawnwolEEwW8SDm180AeM5Zl8nnqVr5ftZ28wiIa1KjCpT2acn7nRvRtXZ/K\nlXSZjUi0UsCLlCMZew7yRWomny/dypx1Oyhy0LROVYb0a8n5XRrRs0VdKlYoqfsJEYk2CniRMLd+\n+wE+T93K50u3smjTbgDaxtfg1oFtGNS5EZ2b1CLQM7SIyH8p4EXCjHOO5Vv38fnSrUxP3cryrfsA\n6NqsNncOas+gzo1oE6+e50Tk6BTwImGgqMixcNNuvkjdyuepW9mwIxsz6J1Qjwd+24nzOjekWV31\nFS8iwVPAi/ik+D3q01O3sm1fLjEVjd+c0oBbzjyFczo2JK6mnssuIidGAS9Shkq6Rz02pgID2sVz\nfpdGDOwQT+2qMX6XKSIRQAEvEmL7cwuYuXwbn6du5Zvl2zhQ7B718zo34sx2ukddREqfAl4kBPYc\nzGd66lamL93Kdz/fo16ZwbpHXUTKiAJepBQ55/goJYOHP0xlx4E83aMuIr5RwIuUkow9B7l/2lK+\nWr6Nbs1q8+oNifRsUUf3qIuILxTwIiepqMgxOXkj//xsOQVFRdx/UUdu7N9KR+si4isFvMhJWJO1\nn3veXULy+p2c1qYB/3vZqbSor/vVRcR/CniRE5BfWMSr367l+a9WEVupAk9d0ZUrezVTc7yIhA0F\nvMhxSknfzd/fSWH51n1cdGpjHrykE/E1Y/0uS0TkFxTwIkE6mFfIM1+uYMz364irWYVXh/TivM6N\n/C5LRKRECniRIPywejv3vLeEjTuzuaZPC+6+oIN6nBORsKaAFzmKPdn5PP7pMqbOSyehfjWmjOpL\n39b1/S5LROSYFPAiR/DZkgz+8UEqu7LzuOXMU7jtnLbExqhLWREpHxTwIofJ3JvDAx8sZXpqJl2a\n1mL8jb3p0rS232WJiBwXBbyIp6jI8da8Tfzvp2nkFRRx9wUdGHlaKypVVJ/xIlL+KOBFgPXbD3D3\neynMXruTvq3r8cTvutKqQXW/yxIROWEKeIlqBYVFjP5uHc/NWEnlShV44nencnXv5uqwRkTKPQW8\nRK2lm/dw17sppG7Zy6DODXlkcBca1lKHNSISGRTwEnVy8gt5bsYqRn+3lrrVKvPSdT254NTGfpcl\nIlKqFPASVWav3cE97y1h3fYDXJXYjPsu7ETtauqwRkQijwJeosKeg/k8+dly3kzeSIt61Zg8Mon+\nbRr4XZaISMiELODNrDkwEWgEFAGvOueeN7N6wFtAArAeuMo5t8sCVzU9D1wIZAPDnHMLQlWfRI/p\nqVv5x/tL2b4/l1FntOZv57SjamV1WCMikS2UR/AFwB3OuQVmVhOYb2ZfAsOAr5xzT5rZ3cDdwF3A\nBUBbb0gCXvJ+ipyQbftyeOjDVD5dspUOjWry2tBEujar43dZIiJlImQB75zLADK88X1mlgY0BQYD\nA7zVJgDfEAj4wcBE55wDZptZHTNr7L2OSNCcc7w9P53HP0njYH4hdw5qz6gzWhOjDmtEJIqUyTl4\nM0sAegBzgIaHQts5l2Fm8d5qTYFNxTZL9+Yp4CVoG3dkc++0JXy/eju9E+ry5OVdOSWuht9liYiU\nuZAHvJnVAN4FbnPO7T1KByIlLXAlvN4oYBRAixYtSqtMKecKixzjfljH01+soFKFCjx6aReu69OC\nChXUYY2IRKeQBryZxRAI98nOufe82ZmHmt7NrDGwzZufDjQvtnkzYMvhr+mcexV4FSAxMfFXXwAk\n+izbspd73kthcfoezu4Qz2OXdaFx7ap+lyUi4qtQXkVvwBggzTn3TLFFHwJDgSe9nx8Um/8nM5tC\n4OK6PTr/LkdzILeAZ79cybgf11OnagwvXNODi7s2VjezIiKE9gi+PzAEWGJmi7x59xII9qlmNgLY\nCFzpLfuUwC1yqwncJndjCGuTcsw5x/TUTB7+KJWMPTlc06cFd53fnjrVKvtdmohI2AjlVfTfU/J5\ndYCzS1jfAbeGqh6JDOm7snnwg1S+Wr6NDo1q8uK1PejVsp7fZYmIhB31ZCflQn5hEWO+X8fzM1Zh\nBvdd2JFh/RN065uIyBEo4CXszV2/k/umLWFl5n7O69SQBy/pTNM6uohORORoFPAStnYeyOPJz9KY\nOi+dpnWqMvqGRM7t1NDvskREygUFvISdQz3RPfFpGvtyCrjlzFP4y9ltqFZZf64iIsHSJ6aElZWZ\n+7h/2lKS1+8ksWVdHr/sVNo3qul3WSIi5Y4CXsLCwbxCXvh6FaO/XUuN2Eo8dXlXrujVTD3RiYic\nIAW8+O7r5Zk88EEq6bsOckWvZtx7YUfqVdc97SIiJ0MBL77J2HOQhz9cxuepW2kTX4Mpo/rSt3V9\nv8sSEYkICngpcwWFRYz/cT3PfrmSQuf4+/ntGXlaaypX0j3tIiKlRQEvZWrBxl3cN20paRl7Gdg+\njkcGd6F5vWp+lyUiEnEU8FIm9mTn88/py3kzeSMNa8by8vU9GdS5kR4MIyISIgp4CSnnHO8v2szj\nn6Sx80Aew/u34m/ntqNGFf3piYiEkj5lJWTWZO3n/mlL+WntDro1r8P4G/vQpWltv8sSEYkKCngp\ndTn5hfxn5mpenrWWKjEVeOzSLlzTpwUVdU+7iEiZUcBLqZq1MosHPljKhh3ZXNq9Cfdd1Im4mlX8\nLktEJOoo4KVUZO7N4dGPl/FxSgatG1Rn8sgk+rdp4HdZIiJRSwEvJ6WwyDFp9gaenr6C3MIibj+3\nHTef2ZoqlSr6XZqISFRTwMsJW5K+h3unLWHJ5j2c3rYBjw7uQkKD6n6XJSIiKODlBOzNyeeZL1Yy\n8af11K9Rhf+7pge/7dpY97SLiIQRBbwcly9St3L/+0vJ2p/LDX1bcseg9tSKjfG7LBEROYwCXoJy\nILeARz9expS5m+jUuBavDU2ka7M6fpclIiJHoICXY1qwcRd/e2sRG3dm88cBp3DbOe30YBgRkTCn\ngJcjyi8s4sWvV/PizNU0qhXLW6P60adVPb/LEhGRICjgpUTrth/gtrcWsXjTbn7XsykPXdJZ59pF\nRMoRBbz8gnOON5M38ejHy6hcqQL/vrYnF3Vt7HdZIiJynBTw8rPt+3O5+90UZqRt47Q2DXj6ym40\nqh3rd1kiInICFPACwFdpmdz1bgp7cwp44LedGPabBCro4TAiIuWWAj7KZecV8NgnabwxZyMdG9di\n8sjutG9U0++yRETkJCngo9jiTbu57a1FrN9xgJvPaM3t57VTH/IiIhFCAR+FCgqL+M83a3j+q1U0\nrFmFN0b2pd8p9f0uS0RESpECPsps2HGAv721iAUbdzO4exMeGdyF2lV1+5uISKRRwEcJ5xxvz0vn\n4Y9SqVDBeP7q7gzu3tTvskREJEQU8FFg54E87n43hS+WZdKvdX3+dVU3mtSp6ndZIiISQgr4CDdz\nxTb+/k4Ke7Lzue/Cjow4rZVufxMRiQIhe2KImY01s21mtrTYvIfMbLOZLfKGC4stu8fMVpvZCjMb\nFKq6osXBvEIe+GApN46bS71qlfngT/256YzWCncRkSgRyiP48cCLwMTD5j/rnHu6+Awz6wRcDXQG\nmgAzzKydc64whPVFrKWb9/DXKQtZk3WAkae14n8GtSc2Rre/iYhEk5AFvHPuWzNLCHL1wcAU51wu\nsM7MVgN9gJ9CVF5EKixyvDxrDc9+uZIGNaoweWQS/ds08LssERHxgR/n4P9kZjcA84A7nHO7gKbA\n7GLrpHvzfsXMRgGjAFq0aBHiUsuPTTuzuX3qIuau38VFXRvz+KVdqFOtst9liYiIT0J2Dv4IXgJO\nAboDGcC/vPklnRh2Jb2Ac+5V51yicy4xLi4uNFWWI8453pmfzgXPf8fyjH08+/tuvHhND4W7iEiU\nK9MjeOdc5qFxMxsNfOxNpgPNi63aDNhShqWVS7sO5HHf+0v4dMlW+rSqxzNXdaNZ3Wp+lyUiImGg\nTAPezBo75zK8ycuAQ1fYfwi8YWbPELjIri2QXJa1lTffrszif95ezK7sPO6+oAM3nd6airpCXkRE\nPCELeDN7ExgANDCzdOBBYICZdSfQ/L4euBnAOZdqZlOBZUABcKuuoC9ZTn4hT362nPE/rqdtfA3G\nDutNl6a1/S5LRETCjDlX4qnuciExMdHNmzfP7zLKTOqWPdw2ZRGrtu1n2G8SuPuCDrr9TUQkypjZ\nfOdc4rHWU0925UBhkWP0d2v51xcrqFutMhOH9+GMdrrAUEREjkwBH+bSd2Vzx9TFzFm3kwu6NOJ/\nLzuVutV1hbyIiBydAj4M5RYUMnvtTr5Ky2Tags044Okru3F5z6aY6UI6ERE5NgV8mMjal8vM5dv4\nankm363aTnZeIbExFRjQLp77LupI83q6/U1ERIKngPeJc460jH18lZbJjOXbWLxpNwCNa8dyWY+m\nnNOxIf1Oqa+L6ERE5IQo4MtQTn4hP63ZwVfLM/k6bRtb9uQA0K15He44tx1ndYynU+NaaoYXEZGT\npoAPsW17c/h6+TZmpG3jh9XbOZhfSLXKFTm9bQNuO6cdAzrEEV8z1u8yRUQkwijgS5lzjtQte/kq\nLXA+PSV9DwBN61TlysRmnN2xIUmt6qnpXUREQkoBXwoO5hXy45rtzEjbxtfLM8ncm4sZ9GhehzsH\ntefsjvG0b1hTTe8iIlJmFPAnaOueQNP7V2mZ/LBmOzn5RdSoUokz2jXgrA4NGdA+jgY1qvhdpoiI\nRCkFfJCKihxLt+z5+Sh96ea9ADSvV5Wre7fg7I7xJLWqT+VKZf0EXhERkV9TwB9Fdl4B36/aHjhS\nX76NrH25VDDo1bIud53fgXM6xtMmvoaa3kVEJOwo4A+zZfdBvvKa3n9cs4O8giJqVqnEGe3jOKdj\nPGe2i6eeuooVEZEwp4D3zN+wi/vfX0paRqDpPaF+NYb0bcnZHeLp3aoeMRXV9C4iIuWHAt7ToEZl\nasZW4t4LO3BWh4acElddTe8iIlJuKeA9LetXZ+rN/fwuQ0REpFSo3VlERCQCKeBFREQikAJeREQk\nAingRUREIpACXkREJAIp4EVERCKQAl5ERCQCKeBFREQikAJeREQkAingRUREIpACXkREJAIp4EVE\nRCKQAl5ERCQCmXPO7xpOmJllARv8riPEGgDb/S6iHNB+Co72U3C0n4Kj/XRsodhHLZ1zccdaqVwH\nfDQws3nOuUS/6wh32k/B0X4KjvZTcLSfjs3PfaQmehERkQikgBcREYlACvjw96rfBZQT2k/B0X4K\njvZTcLSfjs23faRz8CIiIhFIR/AiIiIRSAHvAzMba2bbzGxpsXndzOwnM1tiZh+ZWS1vfoKZHTSz\nRd7wcrFtennrrzazF8zM/Hg/oVIa+8nMqpnZJ2a23MxSzexJv95PKJTW31KxbT8s/lqRohT/z1U2\ns1fNbKX3N3W5H+8nVEpxP13jrZ9iZp+bWQM/3k+oHM9+8pZ19Zalestjvfmh/Qx3zmko4wE4A+gJ\nLC02by5wpjc+HHjUG08ovt5hr5MM9AMM+Ay4wO/3Fm77CagGDPTGKwPfRdJ+Kq2/JW/574A3jrZO\neR1K8f/cw8Bj3ngFoIHf7y3c9hNQCdh2aN8ATwEP+f3efNxPlYAUoJs3XR+o6I2H9DNcR/A+cM59\nC+w8bHZ74Ftv/EvgqEcGZtYYqOWc+8kF/lImApeWdq1+Ko395JzLds7N9MbzgAVAs1Iu1TelsY8A\nzKwGcDvwWKkWGCZKaz8R+OB+wnvNIudcRHXyUkr7ybyhundEWgvYUpp1+u0499N5QIpzbrG37Q7n\nXGFZfIYr4MPHUuASb/xKoHmxZa3MbKGZzTKz0715TYH0Yuuke/Mi3fHup5+ZWR3gYuCr0JfpqxPZ\nR48C/wKyy6jGcHBc+8n7+wF41MwWmNnbZtawDOv1y3HtJ+dcPvAHYAmBYO8EjCnDev1ypP3UDnBm\nNt37u/m7Nz/kn+EK+PAxHLjVzOYDNYE8b34G0MI514PAEdYb3rmdks7VRMMtEce7nwAws0rAm8AL\nzrm1ZVxzWTuufWRm3YE2zrlp/pTrm+P9W6pEoPXnB+dcT+An4OmyL7vMHe/fUwyBgO8BNCHQPH1P\n2Zdd5o60nyoBpwHXeT8vM7OzKYPP8Eql+WJy4pxzywk05WBm7YCLvPm5QK43Pt/M1hD4RpjOL5ua\nmxFhzWAlOYH9NM/b9FVglXPuuTIvuoydwD7qDfQys/UEPhPizewb59yAsq++7JzAfppPoIXj0Beh\nt4ERZVx2mTuB/WTevDXeNlOBu8u+8rJ1pP1E4LN61qHTOWb2KYHz95MI8We4juDDhJnFez8rAPcD\nh64CjzOzit54a6AtsNY5lwHsM7O+3nmuG4APfCm+DB3vfvKmHwNqA7f5UXNZO4G/pZecc02ccwkE\njjBWRnq4wwntJwd8BAzwXuJsYFkZl13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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19845cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_by_year = data.groupby('year').mean()\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "plt.plot(data_by_year.index.values,data_by_year)\n",
    "plt.title('The passengers of year')\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('passengers')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 乘客在一年中各月份的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:38.577397Z",
     "start_time": "2017-12-21T12:06:38.305932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 12 artists>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y3WqHAgDY6HziOwDAACILAGAAkQUAMIDIAgAYQGQBAAwgsgAABhBZAAADiCwAgAFEFgDA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a195a5978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_by_mouth = data.groupby('month').mean()\n",
    "\n",
    "x = [i+1 for i in range(len(data_by_mouth['passengers']))]\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "plt.bar(x,data_by_mouth['passengers'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 练习2：鸢尾花花型尺寸分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:38.759760Z",
     "start_time": "2017-12-21T12:06:38.738310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sns.load_dataset('iris')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 萼片（sepal）和花瓣（petal）的大小关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:40.039084Z",
     "start_time": "2017-12-21T12:06:39.340274Z"
    },
    "run_control": {
     "marked": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3SdoJuFXSLyLirrr1/iciXl1CfGZmlVd4oVnS9sDLgHcWfSwz62+Lh1dz/rwFBHDdEnjn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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19bbedd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_sepal_length = data['sepal_length']\n",
    "data_sepal_width = data['sepal_width']\n",
    "data_petal_length = data['petal_length']\n",
    "data_petal_width = data['petal_width']\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "def scatterplot(x,y,n,x_data,y_data,x_label,y_label,title):\n",
    "    plt.subplot(x,y,n)\n",
    "    \n",
    "    plt.scatter(x_data,y_data,s=10,color = '#539caf', alpha = 0.75)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)\n",
    "\n",
    "scatterplot(2,2,1,data_sepal_length,data_petal_length,'sepal_length','petal_length','between the size of sepal_length and petal_length')\n",
    "scatterplot(2,2,2,data_sepal_length,data_petal_width,'sepal_length','petal_width','between the size of sepal_length and petal_width')\n",
    "scatterplot(2,2,3,data_sepal_width,data_petal_length,'sepal_width','petal_length','between the size of sepal_width and petal_length')\n",
    "scatterplot(2,2,4,data_sepal_width,data_petal_width,'sepal_width','petal_width','between the size of sepal_width and petal_width')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同种类（species）鸢尾花萼片和花瓣的大小关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:40.227754Z",
     "start_time": "2017-12-21T12:06:40.221288Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "species_count = data.groupby('species').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:41.050246Z",
     "start_time": "2017-12-21T12:06:40.230361Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pi/XA+yPiCknbAaskXRgR1zds978R8coK4jPrrqrygTrnP3WOrU+UfqdZ0kzgZcB3yk7L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\nzOBgWWYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108ca4630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "def scatterplot2(x,y,n,data,x_label,y_label,title):\n",
    "    plt.subplot(x,y,n)\n",
    "    \n",
    "    setosa = data[data.species == 'setosa']\n",
    "    versicolor = data[data.species == 'versicolor']\n",
    "    virginica = data[data.species == 'virginica']\n",
    "    \n",
    "    plt.scatter(setosa[x_label],setosa[y_label],s=10,color = 'g', alpha = 0.75)\n",
    "    plt.scatter(versicolor[x_label],versicolor[y_label],s=10,color = 'b', alpha = 0.75)\n",
    "    plt.scatter(virginica[x_label],virginica[y_label],s=10,color = 'r', alpha = 0.75)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)\n",
    "\n",
    "scatterplot2(2,2,1,data,'sepal_length','petal_length','between the size of sepal_length and petal_length')\n",
    "scatterplot2(2,2,2,data,'sepal_length','petal_width','between the size of sepal_length and petal_width')\n",
    "scatterplot2(2,2,3,data,'sepal_width','petal_length','between the size of sepal_width and petal_length')\n",
    "scatterplot2(2,2,4,data,'sepal_width','petal_width','between the size of sepal_width and petal_width')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同种类鸢尾花萼片和花瓣大小的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:42.212635Z",
     "start_time": "2017-12-21T12:06:41.458255Z"
    },
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal_length\n",
      "sepal_width\n",
      "petal_length\n",
      "petal_width\n",
      "sepal_length\n",
      "sepal_width\n",
      "petal_length\n",
      "petal_width\n",
      "sepal_length\n",
      "sepal_width\n",
      "petal_length\n",
      "petal_width\n"
     ]
    },
    {
     "data": {
      "image/png": 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wh4BvTvByXQS8GegkPUthcNt3AlMr1we+Anw6D7+Z9CS16cCswTgq9vkY6eE7\nk4BrBz+vMSjLWTmug/P4GcDHRyjPys//2RXD3wIOz8NnAkfW2O+SfJxNB64Ets/T51eU2bDHFnAa\n8Ik8fFirlWszypTUo/Ddefjz+dg8GHgN0Fe5PtBOeqzrC0iVkAsqjtmTyP/rFet8J5fffqTnfxd+\nXBbaPXKBbgU+L2kBcDHwCClRXJa/tNuA31cs3wcQEVdK2kHSTqSEe5akF5AOosmjiOMQYL+KisIO\nkqbl4R9HxB+BP0p6ANgFmA38MCL+ACDpRyNs//v57zJybaFgzSzXq0hfHKuArwHzJO0OPBwRTw6p\njL2aXB4R8WNJj9TY7g0RsRpA0k2kpDFQZ0xb656IuDoPnwOcSO3yrDRH0gnAdsCzgduAkY6XSi8n\nJZKr876eRUrMg4Y7tmYDbwOIiJ+1aLmOaZlGeq7HbyR1AC8FvkA6/tpIx2ylF5G+HO4EkHQOMK/G\n5n8QEX8Gbpe0S604GmVcJvyI+LWkg4A3AZ8DLgNui4hXVFtlmPHPAv0R8bb8k2vJKEKZBLxiMIEP\nygfeHysmrSeV9Zb+7B3cxuD6hWpyuV4J/DPpl1IvKfEcyeb/VNX2Xc1wn8NYGRrjE9QuTwAktQP/\nTaqd3iPpJFLtcUsIuCwijq4yf7hja0uOz2aVazPK9CrgjcA64Oek2nkb8LE64qulsgzHpKlxvLbh\n7wY8HRHnkH5mvQyYIekVef5kSftXrHJUnj4beCwiHiM1V/xfnj93lKFcCny4Iq4DRlh+ADg8t/9N\nJTVHDHqCVDtummaWa0TcQ2o+eEFE3EUqq48xfMK/EnhX3vcbgb/I05tehkPsNVh2wNHAdVQvz8rY\nBxPRg/k4GU377nXAwZKen/e1naR9R1hnAPj7vPyhtGa5NqNMrwQ+AlwbEWuAnUm1+aFP8LsD2FvS\nPhXxDWqJMhyXCR/4S+CG/FOyF/g06QNcIOlmUjt55RnxRyRdQ2on7s7T/hP4nKSrSd/Wo3Ec0JVP\nIN1OOjFTVUT8gvRc35tJP6mXktpCIdUaFmnTk7Zjrdnlej3w6zx8FbA7wzcTnEw64XUjcCjwO4CI\neIjUhLFcG08uNtMK4L2SbiE1ISykenmeSf78STW/b5Ca2H5AajfeIjkxzQX68v6vIyWpWk4GDs3l\n+kZS08gTLVauzSjT60lNslfm8VuAWyI3xg+KiLWkJpwf55O2lc/2+BHwtiEnbcfchO9LR9IS0smS\npc2OBUDS1NwmvR3pAJoXETc2O64t1Wrl2mpyc9bFEdHZ5FDqJmkKsD63W78C+FpEjPSrdcyMxzJt\nNeOyDX+cWyxpP9JPzLPGY7ISAkuyAAAE40lEQVS3CWsv4AJJk4A/Ae9vcjzWYBO+hj9akv4J+Jch\nk6+OiH9uRjwThcu1MSRdCOw9ZPL8iLikGfFMBGUoUyd8M7OSGK8nbc3MbAs54ZuZlYQTvgEgaX2+\nZGzwNWsU29ikV8WxImmKpJ/nuI8aMq+hvZBK+i9Jt+W/J0ka7uablifprfniASsRJ3wb9IeIOKDi\ntXIU2xhVr4qSRnsfxKADgck57m8PmTeXxvaY+AHgJRHx8QZusyZJRVxN91ZS1wtWIk74VpWktlyT\n/UW+uewDefpUSf+rjb0tviWvskmvihrSb7qk0yTNzcMrlXovHADeIWkfST9T6vHwKg3TO6ikZ0v6\nQY7lOkkvlvQcUp8qB+T97lOxfN29kCr1FHpGfq+/rHhPlfu/CNgeuH6YXxIH5JhukXShpL+Q9BxJ\ny/L8v5IUkvbK479Vuvu1Vo+riyVdCpwtaX9JN+T3cYtSX0VDP6sz881Rt0r61zx9s3JV6pf9COC/\nBstsuPjz+sdJuj1PPz9P2+qeZq1JxqKHNr9a/0XqD+Wm/LowT5sHfDIPTyHdGbw36f6NHfL06cBv\nSH2BzGLzXhUvrhg/DZibh1cCJ1TM+19StwqQunS4fJgYFwKfycOvA24abj9D1llCHb2QAqcC787D\nO5Hu+N1+mO09WTF8Eht7O70FeE0e/nfgS3n4NlIPoh8m3d35LmAm6TZ9qN3j6jJg24r3/q48/KzB\n6RWxHETqO2dwfKda5cqQHiJrxH8vMGXINoftEdWv1n/5xisb9IfY/K7KQ4EXa2Nf3TuSun5dDZwq\n6dXAn0ldIIymt79vQ/rFQLod/jva2CvmlGGWn03qwpmIuFypX/wdR7Hf4XqKPBQ4oqJNvp2cgEfa\nWI5hp4i4Ik86i9T1LaSuew8m9bB4KqnbYbGxj6BaPa5eFBs75rsW6JW0B/D9yD0yVrgLeJ6khcCP\ngUvrLdcR4r+F9AvpB6QuCSAdB2dp63qatSZwwrdaRKoNb3LjSW6WmQEcFBHrJK1k+J4Hn2HTZsOh\nyzyV/04CHh3mC2e4eIYazY0k1XqKfHtE/GoU26vlKuBVpFr9D0n90gep+2mo3ePqYPkQEedJup7U\n4d4lkt4XEZdXzH9E0l8BbyD1Ovr3pA6/6inXWt5M+rI6AviUUsdkjehp1prAbfhWyyXAByVNBpC0\nr6TtSTW8B3Kyn0NKZrB5j4CrSLXXKbkW+frhdhIRjwN3S3pH3o9y8hqqspfM1wIP5nVrqbeXwktI\nbfvK2z+wjnUAiNRL6CPa2CnWPwKDteUrgXcDd0bq+/xhUvfTg32619XjqqTnAXdFxFdIHfC9eMj8\n6cCkiPge8CnSieVa5bqhXKrFr9TFwp4R0Q+cQGrqmkpjepq1JnDCt1q+CdwO3ChpOfB1Uo34XFIv\noUtJCfgO2Ly3ykhdHl9AbhYAflljX+8CupV6PLwN2OykKaldu0upp8T/AN5bx3s4k/p6If0sqWni\nlvxeP1vHtiu9l3QS9BbgAFI7OLHxaqfBnhYHSLXuwYeL1Nvj6lHAcqWeH18EnD1k/u7Akjz/TOAT\neXq1cj0f+Hg+8bpPlfjbgHMk3Ur67L4YEY/SmJ5mrQnctYKZWUm4hm9mVhJO+GZmJeGEb2ZWEk74\nZmYl4YRvZlYSTvhmZiXhhG9mVhL/H3Pn4hzs6eZkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a558978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a558518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AbAssB/o7XirtR0okV+WyNicl5l61jq0ZwFEAEXFRm9brsNZpRDwn6U+SpgH7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a5582e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def boxplot(x_data, y_data, base_color, median_color, x_label, y_label, title):\n",
    "    _, ax = plt.subplots()\n",
    "\n",
    "    bp_data = []\n",
    "    for fearture in x_data:\n",
    "        print(fearture)\n",
    "        bp_data.append(y_data[fearture].values)\n",
    "    # 设置样式\n",
    "    ax.boxplot(bp_data\n",
    "               # 箱子是否颜色填充\n",
    "               , patch_artist = True\n",
    "               # 中位数线颜色\n",
    "               , medianprops = {'color': base_color}\n",
    "               # 箱子颜色设置，color：边框颜色，facecolor：填充颜色\n",
    "               , boxprops = {'color': base_color, 'facecolor': median_color}\n",
    "               # 猫须颜色whisker\n",
    "               , whiskerprops = {'color': median_color}\n",
    "               # 猫须界限颜色whisker cap\n",
    "               , capprops = {'color': base_color})\n",
    "\n",
    "    # 箱图与x_data保持一致\n",
    "    ax.set_xticklabels(x_data)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_title(title)\n",
    "    \n",
    "x_data = [val for idx,val in enumerate(data.columns)][:-1]\n",
    "for val in species_count.index:\n",
    "    boxplot(x_data = x_data, \n",
    "            y_data = data[data.species == val], \n",
    "            base_color = 'b', \n",
    "            median_color = 'r', \n",
    "            x_label = \"Feature of the flowers %s\" % val, \n",
    "            y_label = 'Values', \n",
    "            title = 'The box with 4 feature for %s flowers' % val)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 练习3：餐厅小费情况分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:42.406241Z",
     "start_time": "2017-12-21T12:06:42.355552Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sns.load_dataset(\"tips\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小费和总消费之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:42.774376Z",
     "start_time": "2017-12-21T12:06:42.573059Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zeLCp4etm1st3co39RvViFr/IVrVej/wLwFlgF/CbFcdngePtapQ8q/IPYmDG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\nERGJMQVyERGRGPv/AYhj7hZtv8RwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a199f41d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "def scatterplot3(x_data,y_data,x_label,y_label,title):\n",
    "    plt.subplot()\n",
    "    \n",
    "    plt.scatter(x_data,y_data,color = '#539caf', alpha = 0.75)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)\n",
    "scatterplot3(data['tip'],\n",
    "             data['total_bill'],\n",
    "             'tip',\n",
    "             'total_bill',\n",
    "             'The relationship between tip and total_bill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 男性顾客和女性顾客，谁更慷慨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:43.141614Z",
     "start_time": "2017-12-21T12:06:42.957457Z"
    },
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V8G7gncAqwL3FTCJukLPt0YwZM5g/fz5z5syhpaWFOXPmMH/+fGbMmFF0aMkY9YwgIuY0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\nmJklzonAzCxxTgRmZon7H7se7cIvEmk/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a199df358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def boxplot1( data , x_value , y_value , base_color , median_color ):\n",
    "    _, ax = plt.subplots()\n",
    "\n",
    "    x_feature = data[x_value].unique()\n",
    "    bp_data = []\n",
    "    for val in x_feature:\n",
    "        bp_data.append(data[data[x_value] == val][y_value].values)\n",
    "    \n",
    "    ax.boxplot(bp_data\n",
    "               , patch_artist = True\n",
    "               , medianprops = {'color': base_color}\n",
    "               , boxprops = {'color': base_color, 'facecolor': median_color}\n",
    "               , whiskerprops = {'color': median_color}\n",
    "               , capprops = {'color': base_color})\n",
    "\n",
    "    ax.set_xticklabels(x_feature)\n",
    "    ax.set_ylabel('Values of %s' % y_value)\n",
    "    ax.set_xlabel('Features of the %s' % x_value)     \n",
    "    ax.set_title('The box with %d features for the %s' % (len(x_feature),y_value))\n",
    "\n",
    "boxplot1( data = data ,\n",
    "         x_value = 'sex' ,\n",
    "         y_value = 'tip',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 抽烟与否是否会对小费金额产生影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:43.344711Z",
     "start_time": "2017-12-21T12:06:43.144915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PA3OA1YB7ixkjpk6dygknnLBdV5UnnHACU6dOzTo0M1paWqipqUESNTU1tLS0ZB1SrgyZ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\nE4GZWc45EZiZ5ZwTgZlZzjkRmJnl3P8CfB32MMzJHV8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a199f4ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot1( data = data ,\n",
    "         x_value = 'smoker' ,\n",
    "         y_value = 'tip',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 工作日和周末，什么时候顾客给的小费更慷慨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:43.953171Z",
     "start_time": "2017-12-21T12:06:43.681932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4xsVFv/9+NMfDVUNWtPGqho4A/rbiDHk7sm6V7wU+J+kQ4HlgV7Iv4Y11AWRX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a620e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot1( data = data ,\n",
    "         x_value = 'day' ,\n",
    "         y_value = 'tip',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:43.974124Z",
     "start_time": "2017-12-21T12:06:43.956732Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>17.682742</td>\n",
       "      <td>2.771452</td>\n",
       "      <td>2.451613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>17.151579</td>\n",
       "      <td>2.734737</td>\n",
       "      <td>2.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>20.441379</td>\n",
       "      <td>2.993103</td>\n",
       "      <td>2.517241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>21.410000</td>\n",
       "      <td>3.255132</td>\n",
       "      <td>2.842105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      total_bill       tip      size\n",
       "day                                 \n",
       "Thur   17.682742  2.771452  2.451613\n",
       "Fri    17.151579  2.734737  2.105263\n",
       "Sat    20.441379  2.993103  2.517241\n",
       "Sun    21.410000  3.255132  2.842105"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_mean = data.groupby('day').mean()\n",
    "day_mean    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:44.347681Z",
     "start_time": "2017-12-21T12:06:44.198053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K8g7gr4D9gEuq6q4kHwGmqmoz8M4kPwvsAh4CzuqrHknS/HoLBYCquhq4emTduUPL7wPe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\nVZ+seIjEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c90a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def barplot(x_data, y_data, x_label, y_label, title):\n",
    "    _, ax = plt.subplots()\n",
    "    ax.bar(x_data, y_data, color = '#539caf', align = 'center')\n",
    "    # 绘制方差\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_title(title)\n",
    "\n",
    "barplot(x_data = day_mean.index\n",
    "        , y_data = day_mean['tip']\n",
    "        , x_label = 'day'\n",
    "        , y_label = 'tip'\n",
    "        , title = 'the tip of day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 午饭和晚饭，哪一顿顾客更愿意给小费"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:45.014040Z",
     "start_time": "2017-12-21T12:06:44.738617Z"
    },
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SQkmfBn4uab6k+ZMP1erBQ1XadDUwMMCSJUt2KluyZAkDAwM5RVQ81YxQdlz6++Gy8g8B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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c93ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot1( data = data ,\n",
    "         x_value = 'time' ,\n",
    "         y_value = 'tip',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 就餐人数是否会对慷慨度产生影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:45.214512Z",
     "start_time": "2017-12-21T12:06:45.198381Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.242500</td>\n",
       "      <td>1.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.448013</td>\n",
       "      <td>2.582308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.277632</td>\n",
       "      <td>3.393158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.613514</td>\n",
       "      <td>4.135405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30.068000</td>\n",
       "      <td>4.028000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34.830000</td>\n",
       "      <td>5.225000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      total_bill       tip\n",
       "size                      \n",
       "1       7.242500  1.437500\n",
       "2      16.448013  2.582308\n",
       "3      23.277632  3.393158\n",
       "4      28.613514  4.135405\n",
       "5      30.068000  4.028000\n",
       "6      34.830000  5.225000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size_mean = data.groupby('size').mean()\n",
    "size_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:45.379082Z",
     "start_time": "2017-12-21T12:06:45.217592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gM/D44Xa152pgR3d3Wfe14GcuSVYALwLOGnYvergkvwQ8G1gPUFU/W0hhDwZ+c5KsAo4A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c57780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot(x_data = size_mean.index\n",
    "        , y_data = size_mean['tip']\n",
    "        , x_label = 'size'\n",
    "        , y_label = 'tip'\n",
    "        , title = 'the tip of size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:46.078583Z",
     "start_time": "2017-12-21T12:06:45.824456Z"
    },
    "code_folding": [],
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KSW8b6/mI+H5VIyvgpqGpa2tro7e3l46Ojs3LBgYG6OrqYs2aNRlGVmFuGqpL/f399PT0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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ad74cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot1( data = data ,\n",
    "         x_value = 'size' ,\n",
    "         y_value = 'tip',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 性别+抽烟的组合因素对慷慨度的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:46.349657Z",
     "start_time": "2017-12-21T12:06:46.273932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3.051167</td>\n",
       "      <td>3.113402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2.931515</td>\n",
       "      <td>2.773519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker       Yes        No\n",
       "sex                       \n",
       "Male    3.051167  3.113402\n",
       "Female  2.931515  2.773519"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_by_sex_smoker = pd.pivot_table(data=data,\n",
    "                                    values='tip',\n",
    "                                    index='sex',\n",
    "                                    columns='smoker',\n",
    "                                    fill_value=0,\n",
    "                                    aggfunc='mean'\n",
    "                                   )\n",
    "mean_by_sex_smoker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:46.381022Z",
     "start_time": "2017-12-21T12:06:46.355933Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def groupedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):\n",
    "    _, ax = plt.subplots()\n",
    "    # 设置每一组柱状图的宽度\n",
    "    total_width = 0.8\n",
    "    # 设置每一个柱状图的宽度\n",
    "    ind_width = total_width / len(y_data_list)\n",
    "    # 计算每一个柱状图的中心偏移\n",
    "    alteration = np.arange(-total_width/2+ind_width/2,\n",
    "                           total_width/2+ind_width/2, ind_width)\n",
    "    # 分别绘制每一个柱状图\n",
    "    for i in range(0, len(y_data_list)):\n",
    "        # 横向散开绘制\n",
    "        ax.bar(x_data + alteration[i], \n",
    "               y_data_list[i], \n",
    "               color = colors[i],\n",
    "               label = y_data_names[i], \n",
    "               width = ind_width)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_title(title)\n",
    "    ax.set_xticks(np.linspace(0,1,len(mean_by_sex_smoker)))\n",
    "    ax.set_xticklabels(mean_by_sex_smoker.index)\n",
    "    ax.legend(loc = 'upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:46.882083Z",
     "start_time": "2017-12-21T12:06:46.657826Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y2TqvOySEVqv7WZwUzMys4HMKZmYVDpvYuU+Lv/Gktp/UHRHssccenH322Rx4YHoFyuTJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1aee69e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupedbarplot([i for i in range(len(mean_by_sex_smoker.index.values))], \n",
    "               y_data_list = [mean_by_sex_smoker['Yes'],mean_by_sex_smoker['No']], \n",
    "               y_data_names = ['Yes','No'],\n",
    "               colors = ['#539caf','#7663b0'],\n",
    "               x_label = 'Sex', y_label = 'Value of tip', title = 'Values by Sex (Male of Female) and Smoker (Yes or No)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 练习4：泰坦尼克号海难幸存状况分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:47.351622Z",
     "start_time": "2017-12-21T12:06:47.294855Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sns.load_dataset(\"titanic\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同仓位等级中幸存和遇难的乘客比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:47.636678Z",
     "start_time": "2017-12-21T12:06:47.604507Z"
    },
    "collapsed": true,
    "run_control": {
     "marked": true
    }
   },
   "outputs": [],
   "source": [
    "data_survived_ornot = data[['survived','pclass']].groupby('pclass').sum()\n",
    "data_survived_ornot['total'] = data['pclass'].value_counts()\n",
    "data_survived_ornot['unsurvived'] = data_survived_ornot['total'] - data_survived_ornot['survived']\n",
    "data_survived_ornot['survived_prop'] = data_survived_ornot['survived']/data_survived_ornot['total']\n",
    "data_survived_ornot['unsurvived_prop'] = data_survived_ornot['unsurvived']/data_survived_ornot['total']\n",
    "y_data_list = [data_survived_ornot['survived_prop'],data_survived_ornot['unsurvived_prop']]\n",
    "y_data_names = ['survived','unsurvived']\n",
    "colors = ['b','r']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:48.125121Z",
     "start_time": "2017-12-21T12:06:47.843763Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mZhWU5emjiyW9IulcSf1yj8jMzComy3sKPwQGAfOBMZJe8HgKZmZtU6b3FCLi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1afd15f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def stackedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):\n",
    "    _, ax = plt.subplots()\n",
    "    # 循环绘制堆积柱状图\n",
    "    for i in range(0, len(y_data_list)):\n",
    "        if i == 0:\n",
    "            ax.bar(x_data, y_data_list[i], color = colors[i], align = 'center', label = y_data_names[i])\n",
    "        else:\n",
    "            # 采用堆积的方式，除了第一个分类，后面的分类都从前一个分类的柱状图接着画\n",
    "            # 用归一化保证最终累积结果为1，下面bottom参数表示纵向从哪里开始画\n",
    "            ax.bar(x_data, y_data_list[i], color = colors[i], bottom = y_data_list[i - 1], align = 'center', label = y_data_names[i])\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_title(title)\n",
    "    ax.legend(loc = 'upper right') # 设定图例位置\n",
    "stackedbarplot(data_survived_ornot.index,\n",
    "               y_data_list,\n",
    "               y_data_names,\n",
    "               colors,'pclass','survived/unsurvived values','proportion of survived or unsurvived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同性别的幸存比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:48.476761Z",
     "start_time": "2017-12-21T12:06:48.448295Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_survived_ornot = data[['survived','sex']].groupby('sex').sum()\n",
    "data_survived_ornot['total'] = data['sex'].value_counts()\n",
    "data_survived_ornot['unsurvived'] = data_survived_ornot['total'] - data_survived_ornot['survived']\n",
    "data_survived_ornot['survived_prop'] = data_survived_ornot['survived']/data_survived_ornot['total']\n",
    "data_survived_ornot['unsurvived_prop'] = data_survived_ornot['unsurvived']/data_survived_ornot['total']\n",
    "y_data_list = [data_survived_ornot['survived_prop'],data_survived_ornot['unsurvived_prop']]\n",
    "y_data_names = ['survived','unsurvived']\n",
    "colors = ['b','r']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:48.703907Z",
     "start_time": "2017-12-21T12:06:48.482371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cIzIzs4rJ8p7Cd4G+wHxglKSX3Z+CmVnzlOk9hYh4NyKuBE4heTz1glyjMjOz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\noRR3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b07d160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stackedbarplot(data_survived_ornot.index,\n",
    "               y_data_list,\n",
    "               y_data_names,\n",
    "               colors,'sex','survived/unsurvived values','proportion of survived or unsurvived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 幸存和遇难乘客的票价分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:49.265383Z",
     "start_time": "2017-12-21T12:06:49.087131Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b09bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot1( data = data ,\n",
    "         x_value = 'survived' ,\n",
    "         y_value = 'fare',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 幸存和遇难乘客的年龄分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:06:49.707643Z",
     "start_time": "2017-12-21T12:06:49.458878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rqY0bNzJ58mSmTZvGoEGDmDZtGpMnT2bjxo21Dq0wnAjMrKYGDhzI6NGjWbt2LRHB2rVr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c90470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot1( data = data[['survived','age']].dropna() ,\n",
    "         x_value = 'survived' ,\n",
    "         y_value = 'age',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同上船港口的乘客仓位等级分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:37:54.562014Z",
     "start_time": "2017-12-21T12:37:54.539917Z"
    },
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embarked</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>85</td>\n",
       "      <td>17</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>127</td>\n",
       "      <td>164</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "pclass      1    2    3\n",
       "embarked               \n",
       "C          85   17   66\n",
       "Q           2    3   72\n",
       "S         127  164  353"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size_by_embarked_pclass = pd.pivot_table(data=data,\n",
    "                                    index='embarked',\n",
    "                                    columns='pclass',\n",
    "                                    aggfunc='size'\n",
    "                                   )\n",
    "y_data_names = [idx for idx in size_by_embarked_pclass.index]\n",
    "size_by_embarked_pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:38:22.506015Z",
     "start_time": "2017-12-21T12:38:22.225490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mf2e7FFmp1hJE3s+12GAmYjIwuWvCqR2SGiNidLXjsOL8nXUv/r42Vmud1E3A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\nrtoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1adbb7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def groupedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):\n",
    "    _, ax = plt.subplots()\n",
    "    # 设置每一组柱状图的宽度\n",
    "    total_width = 0.8\n",
    "    # 设置每一个柱状图的宽度\n",
    "    ind_width = total_width / len(y_data_list)\n",
    "    # 计算每一个柱状图的中心偏移\n",
    "    alteration = np.arange(-total_width/2+ind_width/2,\n",
    "                           total_width/2+ind_width/2, ind_width)\n",
    "    # 分别绘制每一个柱状图\n",
    "    for i in range(0, len(y_data_list)):\n",
    "        # 横向散开绘制\n",
    "        ax.bar(x_data + alteration[i], \n",
    "               y_data_list[i], \n",
    "               color = colors[i],\n",
    "               label = y_data_names[i], \n",
    "               width = ind_width)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_title(title)\n",
    "    ax.set_xticks(np.linspace(0,2,len(size_by_embarked_pclass)))\n",
    "    ax.set_xticklabels(size_by_embarked_pclass.columns)\n",
    "    ax.legend(loc = 'upper right')\n",
    "\n",
    "groupedbarplot([i for i in range(len(size_by_embarked_pclass.index.values))], \n",
    "               y_data_list = [size_by_embarked_pclass[1],\n",
    "                              size_by_embarked_pclass[2],\n",
    "                              size_by_embarked_pclass[3]], \n",
    "               y_data_names = ['C','Q','S'],\n",
    "               colors = ['#539caf','#7663b0','#1263bc'],\n",
    "               x_label = 'Embarked', y_label = 'Value of pclass', title = 'Values by Embarked and Pclass')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 幸存和遇难乘客堂兄弟姐妹的数量分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:31:31.460018Z",
     "start_time": "2017-12-21T12:31:31.266910Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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er4xunP7OBaZJuhr4bSfb2B+YHlmT0NOSbgfeRc/fN/S7iHgNeEBS+74ckoa/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1aba2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot1( data = data[['survived','sibsp']].dropna() ,\n",
    "         x_value = 'survived' ,\n",
    "         y_value = 'sibsp',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 幸存和遇难乘客父母子女的数量分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:32:15.310188Z",
     "start_time": "2017-12-21T12:32:15.132753Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b6X1XAkf3Wddk4NqS8THp3+uA1pJ9tDa9fTrJ+ZH2Scf/BfhBentXkjOj7la6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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ade8e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot1( data = data[['survived','parch']].dropna() ,\n",
    "         x_value = 'survived' ,\n",
    "         y_value = 'parch',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单独乘船与否和幸存之间有没有联系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:54:56.076230Z",
     "start_time": "2017-12-21T12:54:56.055942Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alone</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>175</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>374</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "survived    0    1\n",
       "alone             \n",
       "False     175  179\n",
       "True      374  163"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size_by_alone_survived = pd.pivot_table(data=data,\n",
    "                                    index='alone',\n",
    "                                    columns='survived',\n",
    "                                    aggfunc='size'\n",
    "                                   ) \n",
    "size_by_alone_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-21T12:55:20.906772Z",
     "start_time": "2017-12-21T12:55:20.685786Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1aec7128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def groupedbarplot(x_data, y_data_list, y_data_names, colors, x_label, y_label, title):\n",
    "    _, ax = plt.subplots()\n",
    "    # 设置每一组柱状图的宽度\n",
    "    total_width = 0.8\n",
    "    # 设置每一个柱状图的宽度\n",
    "    ind_width = total_width / len(y_data_list)\n",
    "    # 计算每一个柱状图的中心偏移\n",
    "    alteration = np.arange(-total_width/2+ind_width/2,\n",
    "                           total_width/2+ind_width/2, ind_width)\n",
    "    # 分别绘制每一个柱状图\n",
    "    for i in range(0, len(y_data_list)):\n",
    "        # 横向散开绘制\n",
    "        ax.bar(x_data + alteration[i], \n",
    "               y_data_list[i], \n",
    "               color = colors[i],\n",
    "               label = y_data_names[i], \n",
    "               width = ind_width)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_title(title)\n",
    "    ax.set_xticks(np.linspace(0,1,len(size_by_alone_survived)))\n",
    "    ax.set_xticklabels(size_by_alone_survived.index)\n",
    "    ax.legend(loc = 'upper right')\n",
    "\n",
    "groupedbarplot([i for i in range(len(size_by_alone_survived.index.values))], \n",
    "               y_data_list = [size_by_alone_survived[0],\n",
    "                              size_by_alone_survived[1]], \n",
    "               y_data_names = ['0','1'],\n",
    "               colors = ['#539caf','#7663b0','#1263bc'],\n",
    "               x_label = 'alone', y_label = 'The number of pclass', title = 'The number of pepole by whether be survived or alone or not')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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